Template generation using directed acyclic word graphs

ABSTRACT

Technologies for template generation using directed acyclic word graphs (DAWGs). The technologies can include receiving a first plurality of titles from a first plurality of title feeds, and sorting the first plurality of titles into a plurality of category sets. And, for each category set of the plurality of category sets, the technologies can include transforming the respective titles belonging to the category set into a trie data structure by separating words in the respective titles into nodes of the trie data structure. For each category set, the technologies can also include transforming the trie data structure into a directed acyclic word graph (DAWG) data structure. Also, for each category set, the technologies can also include generating one or more unique templates based on the DAWG data structure.

This application includes material that is subject to copyrightprotection. The copyright owner has no objection to the facsimilereproduction by anyone of the patent disclosure, as it appears in thePatent and Trademark Office files or records, but otherwise reserves allcopyright rights whatsoever.

FIELD

The present disclosure relates to template generation using directedacyclic word graphs (DAWGs).

BACKGROUND

Entity resolution and developing a deeper understanding of entity titles(such as product titles) across the Internet is valuable in severalapplications ranging from search engines to analytics. Unfortunately,entity titles (such as product titles) are not standardized over theInternet for the most part. The titles lack structure and have severalvariations, including variation in spelling and sequence. Some websitesallow human generated titles for products or services, which oftencauses a lot of variance. And, even among sophisticated websites thatare more automated, there is still variance in entity and producttitles.

In a typical entity resolution setting, a developer can train models toassign labels that identify common parts of a certain type of entitytitle. Such models are considered supervised models and can useconditional random fields (CRFs).

For a product title there are common parts, such as ‘Brand’, ‘ProductName’, ‘Shape’, and ‘Size’. The product names usually stem fromtemplatized rules that standardize representation of entities.Unfortunately, the templates tend to vary from website to website. It ispossible to represent such variants through a common aggregated itemname through entity extraction. Entity extraction can be used toidentify similar product by extracting different parts of a producttitle. The products can then be grouped using the entity labels.Supervised learning techniques can be used for such tasks.

Most common supervised learning approaches use sequential labeling andother technologies (such as CRFs) to assign labels to the tokens. Mostsequence based approaches are interested in attribute extraction fromproduct titles, such as product titles that include ‘Brand’, ‘ProductName’, ‘Shape’, and ‘Size’. While supervised learning approaches canlearn labels, they have to be constantly retrained with new label datato capture new products or variations in entity titles. This problem canbe due to a rigidity of supervised learning in that it is sequential.This can make training supervised models challenging. Further, producttitles are not as standardized as one may expect; thus, titles oftenlack structure and have much variation.

Variation in entity and product titles can be a result of severalfactors. Different websites can follow their own naming scheme, even ifsuch websites are selling or promoting the same product or service.Moreover, open marketplaces allow sellers to name their products,leading to greater variation as well.

A few approaches have attempted to resolve the aforesaid variationissues starting with a supervised sequential learning approach to obtainan initial set of labeled items, then using a bootstrapping approach togrow the initial set. Such semi-supervised approaches are helpful whenhandling large datasets with high levels of variations in entity andproduct titles, but do not solve the variation issue. While thevariation issue has been studied and understood as a part of activelearning and semi-supervised learning research, few supervised orsemi-supervised approaches can solve the overwhelming variation issuedue to rigidity in these approaches in that they are sequential based.

On the other hand, unsupervised approaches tend to be based on a bag ofwords, and such models, unlike their supervised learning counter parts,do not consider the sequence of words in the entity and product titles.This more flexible approach may seem beneficial, but it can be hard toobtain a coherent label using the output from bag of words models asthey lose the sentence-like structure in a title. Moreover, while suchapproaches are effective in capturing the head terms of a title,clustering of such models fails in consolidating the tail terms.

Alternative approaches to entity recognition from titles includedictionary based lookups and rule based extractions. These techniqueshave been applied with limited success as well; and even whensuccessful, their usage is restricted to cover smaller datasets. Thus,technical problems persist that prevent search, e-commerce, research,and other data extraction based applications less efficient andeffective.

SUMMARY

Described herein are improved systems and methods for templategeneration using directed acyclic word graphs (DAWGs), which canovercome at least the technical problems mentioned in the backgroundsection above, such as the variation issues that have not been remediedby supervised and semi-supervised data extraction models.

Disclosed herein is an unsupervised approach that can use a DAWG togroup similar entity titles (such as product titles) and to summarizethe variation in the titles. The variations provided through a DAWG canreduce mislabeling commonly associated with supervised approaches aswell as essentially eliminate or at least reduce variance in titles.This is very useful in improving efficiency and effectiveness in manytechnical applications such as applications ranging from search enginesto e-commerce to recommendation systems to analytics that can depend onsome form of entity extraction.

DAWG can be used to group similar entity titles in a scalable way. And,the grouping can limit variance in entity titles virtually; and thus,improve technical applications reliant on entity titles and that areimproved when variance is reduced or eliminated in entity titles. Thetechnologies described herein can be applied to such applications andvirtually reduce variance in entity titles through grouping the titlesaccording to templates that are generated using DAWGs.

In general, the unsupervised approach disclosed herein is meant tooperate on millions of entity and product titles as there are as many inreality due to the variations from website to website and feed to feed.The disclosed technical solution ensures that the items that can becombined together are part of a same trie. In addition to theunsupervised approach using DAWG described herein DAWG can be applied toonly the most frequent or common item names (also referred to as thehead of a title). In some embodiments, partial and/or entire titles canbe applied to the DAWG based approach disclosed herein.

Also, templates can be extracted from the DAWG data structure andapplied to various applications such as search and analytics. Onceequipped with DAWG canonicalized templates, the methods and systems canmatch any item or title to one or more templates. Regular expressionmatching of the token words can be used.

In summary, examples of the systems and methods disclosed herein fortemplate generation using DAWGs provide specific technical solutions toat least overcome the technical problems mentioned in the backgroundsection and other parts of the application as well as other technicalproblems not described herein but recognized by those of skill in theart.

In accordance with one or more embodiments, this disclosure providescomputerized methods for template generation using DAWGs, as well as anon-transitory computer-readable storage medium for carrying outtechnical steps of the computerized methods. The non-transitorycomputer-readable storage medium has tangibly stored thereon, ortangibly encoded thereon, computer readable instructions that whenexecuted by one or more devices (e.g., one or more servers) cause atleast one processor to perform a method for a novel and improvedtemplate generation using DAWGs.

In accordance with one or more embodiments, a system is provided thatincludes one or more computing devices configured to providefunctionality in accordance with one or more embodiments of a novel andimproved way of template generation using DAWGs.

In accordance with one or more embodiments, functionality is embodied insteps of a method performed by at least one computing device. Inaccordance with one or more embodiments, program code (or program logic)executed by processor(s) of a computing device to implementfunctionality in accordance with one or more embodiments describedherein is embodied in, by and/or on a non-transitory computer-readablemedium.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing and other objects, features, and advantages of thedisclosure will be apparent from the following description ofembodiments as illustrated in the accompanying drawings, in whichreference characters refer to the same parts throughout the variousviews. The drawings are not necessarily to scale, emphasis instead beingplaced upon illustrating principles of the disclosure:

FIG. 1 is a schematic diagram illustrating an example of a network(which includes elements that can implement template generation usingDAWGs) within which systems and methods disclosed herein can beimplemented according to some embodiments of the present disclosure.

FIG. 2 is a schematic diagram illustrating an example of a computingdevice, in accordance with some embodiments of the present disclosure.

FIGS. 3 and 4 are flowcharts illustrating example methods, in accordancewith some embodiments of the present disclosure.

FIG. 5 illustrates a normalized trie data structure, in accordance withsome embodiments of the present disclosure.

FIG. 6 illustrates a DAWG data structure based on the normalized triedata structure depicted in FIG. 5, in accordance with some embodimentsof the present disclosure.

DESCRIPTION OF EMBODIMENTS

The present disclosure will now be described more fully hereinafter withreference to the accompanying drawings, which form a part hereof, andwhich show, by way of illustration, certain example embodiments. Subjectmatter may, however, be embodied in a variety of different forms and,therefore, covered or claimed subject matter is intended to be construedas not being limited to any example embodiments set forth herein;example embodiments are provided merely to be illustrative. Likewise, areasonably broad scope for claimed or covered subject matter isintended. Among other things, for example, subject matter may beembodied as methods, devices, components, or systems. Accordingly,embodiments may, for example, take the form of hardware, software,firmware or any combination thereof (other than software per se). Thefollowing detailed description is, therefore, not intended to be takenin a limiting sense.

Throughout the specification and claims, terms may have nuanced meaningssuggested or implied in context beyond an explicitly stated meaning.Likewise, the phrase “in one embodiment” as used herein does notnecessarily refer to the same embodiment and the phrase “in anotherembodiment” as used herein does not necessarily refer to a differentembodiment. It is intended, for example, that claimed subject matterinclude combinations of example embodiments in whole or in part.

In general, terminology may be understood at least in part from usage incontext. For example, terms, such as “and”, “or”, or “and/or,” as usedherein may include a variety of meanings that may depend at least inpart upon the context in which such terms are used. Typically, “or” ifused to associate a list, such as A, B or C, is intended to mean A, B,and C, here used in the inclusive sense, as well as A, B or C, here usedin the exclusive sense. In addition, the term “one or more” as usedherein, depending at least in part upon context, may be used to describeany feature, structure, or characteristic in a singular sense or may beused to describe combinations of features, structures or characteristicsin a plural sense. Similarly, terms, such as “a,” “an,” or “the,” again,may be understood to convey a singular usage or to convey a pluralusage, depending at least in part upon context. In addition, the term“based on” may be understood as not necessarily intended to convey anexclusive set of factors and may, instead, allow for existence ofadditional factors not necessarily expressly described, again, dependingat least in part on context.

The present disclosure is described below with reference to blockdiagrams and operational illustrations of methods and devices. It isunderstood that each block of the block diagrams or operationalillustrations, and combinations of blocks in the block diagrams oroperational illustrations, can be implemented by means of analog ordigital hardware and computer program instructions. These computerprogram instructions can be provided to a processor of a general-purposecomputer to alter its function as detailed herein, a special purposecomputer, ASIC, or other programmable data processing apparatus, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, implement thefunctions/acts specified in the block diagrams or operational block orblocks. In some alternate implementations, the functions/acts noted inthe blocks can occur out of the order noted in the operationalillustrations. For example, two blocks shown in succession can in factbe executed substantially concurrently or the blocks can sometimes beexecuted in the reverse order, depending upon the functionality/actsinvolved.

These computer program instructions can be provided to a processor of: ageneral purpose computer to alter its function to a special purpose; aspecial purpose computer; ASIC; or other programmable digital dataprocessing apparatus, such that the instructions, which execute via theprocessor of the computer or other programmable data processingapparatus, implement the functions/acts specified in the block diagramsor operational block or blocks, thereby transforming their functionalityin accordance with embodiments herein.

For the purposes of this disclosure a computer readable medium (orcomputer-readable storage medium/media) stores computer data, which datacan include computer program code (or computer-executable instructions)that is executable by a computer, in machine readable form. By way ofexample, and not limitation, a computer readable medium can includecomputer readable storage media, for tangible or fixed storage of data,or communication media for transient interpretation of code-containingsignals. Computer readable storage media, as used herein, refers tophysical or tangible storage (as opposed to signals) and includeswithout limitation volatile and non-volatile, removable andnon-removable media implemented in any method or technology for thetangible storage of information such as computer-readable instructions,data structures, program modules or other data. Computer readablestorage media includes, but is not limited to, RAM, ROM, EPROM, EEPROM,flash memory or other solid-state memory technology, CD-ROM, DVD, orother optical storage, magnetic cassettes, magnetic tape, magnetic diskstorage or other magnetic storage devices, or any other physical ormaterial medium which can be used to tangibly store the desiredinformation or data or instructions and which can be accessed by acomputer or processor.

For the purposes of this disclosure the term “server” should beunderstood to refer to a service point which provides processing,database, and communication facilities. By way of example, and notlimitation, the term “server” can refer to a single, physical processorwith associated communications and data storage and database facilities,or it can refer to a networked or clustered complex of processors andassociated network and storage devices, as well as operating softwareand one or more database systems and application software that supportthe services provided by the server. Servers can vary widely inconfiguration or capabilities, but generally a server can include one ormore central processing units and memory. A server can also include oneor more mass storage devices, one or more power supplies, one or morewired or wireless network interfaces, one or more input/outputinterfaces, or one or more operating systems, such as Windows Server,Mac OS X, Unix, Linux, FreeBSD, or the like.

For the purposes of this disclosure a “network” should be understood torefer to a network that can couple devices so that communications can beexchanged, such as between a server and a client device or other typesof devices, including between wireless devices coupled via a wirelessnetwork, for example. A network can also include mass storage, such asnetwork attached storage (NAS), a storage area network (SAN), or otherforms of computer or machine-readable media, for example. A network caninclude the Internet, one or more local area networks (LANs), one ormore wide area networks (WANs), wire-line type connections, wirelesstype connections, cellular or any combination thereof. Likewise,sub-networks, which can employ differing architectures or can becompliant or compatible with differing protocols, can interoperatewithin a larger network. Various types of devices can, for example, bemade available to provide an interoperable capability for differingarchitectures or protocols. As one illustrative example, a router canprovide a link between otherwise separate and independent LANs.

A communication link or channel can include, for example, analogtelephone lines, such as a twisted wire pair, a coaxial cable, full orfractional digital lines including T1, T2, T3, or T4 type lines,Integrated Services Digital Networks (ISDNs), Digital Subscriber Lines(DSLs), wireless links including satellite links, or other communicationlinks or channels, such as can be known to those skilled in the art.Furthermore, a computing device or other related electronic devices canbe remotely coupled to a network, such as via a wired or wireless lineor link, for example.

A computing device can be capable of sending or receiving signals, suchas via a wired or wireless network, or can be capable of processing orstoring signals, such as in memory as physical memory states, and can,therefore, operate as a server. Thus, devices capable of operating as aserver can include, as examples, dedicated rack mounted servers, desktopcomputers, laptop computers, set top boxes, integrated devices combiningvarious features, such as two or more features of the foregoing devices,or the like. Servers can vary widely in configuration or capabilities,but generally a server can include one or more central processing unitsand memory. A server can also include one or more mass storage devices,one or more power supplies, one or more wired or wireless networkinterfaces, one or more input/output interfaces, or one or moreoperating systems, such as Windows Server, Mac OS X, Unix, Linux,FreeBSD, or the like.

For purposes of this disclosure, a client (or consumer or user) devicecan include a computing device capable of sending or receiving signals,such as via a wired or a wireless network. A client device can, forexample, include a desktop computer or a portable device, such as acellular telephone, a smart phone, a display pager, a radio frequency(RF) device, an infrared (IR) device, an NFC device, a Personal DigitalAssistant (PDA), a handheld computer, a tablet computer, a phablet, alaptop computer, a set top box, a wearable computer, smart watch, anintegrated or distributed device combining various features, such asfeatures of the forgoing devices, or the like.

A client device can vary in terms of capabilities or features. Claimedsubject matter is intended to cover a wide range of potentialvariations. For example, a simple smart phone, phablet or tablet caninclude a numeric keypad or a display of limited functionality, such asa monochrome liquid crystal display (LCD) for displaying text. Incontrast, however, as another example, a web-enabled client device caninclude a high-resolution screen, one or more physical or virtualkeyboards, mass storage, one or more accelerometers, one or moregyroscopes, global positioning system (GPS) or otherlocation-identifying type capability, or a display with a high degree offunctionality, such as a touch-sensitive color 2D or 3D display, forexample.

A client device can include or can execute a variety of operatingsystems, including a personal computer operating system, such as aWindows, iOS or Linux, or a mobile operating system, such as iOS,Android, or Windows Mobile, or the like.

A client device can include or can execute a variety of possibleapplications, such as a client software application enablingcommunication with other devices, such as communicating one or moremessages, such as via email, for example Yahoo! ® Mail, short messageservice (SMS), or multimedia message service (MMS), for example Yahoo!Messenger®, including via a network, such as a social network,including, for example, Tumblr®, Facebook®, LinkedIn®, Twitter®,Flickr®, or Google+®, Instagram™, to provide only a few possibleexamples. A client device can also include or execute an application tocommunicate content, such as, for example, textual content, multimediacontent, or the like. A client device can also include or execute anapplication to perform a variety of possible tasks, such as browsing,searching, playing, streaming or displaying various forms of content,including locally stored or uploaded images and/or video, or games (suchas fantasy sports leagues). The foregoing is provided to illustrate thatclaimed subject matter is intended to include a wide range of possiblefeatures or capabilities.

In developing a model to consolidate variations in entity and producttitles, it is important to consider the following requirements: abilityto cover multiple variations across several websites, scalability tocover millions of entity and product titles, and ability to capturefuture variations. Disclosed herein is an unsupervised approach usingdirected acyclic word graphs (DAWGs) in a certain way to handle thevariations in entity and product titles and attempt to comply with theaforesaid requirements.

An example objective of the technical solution described herein is notonly to capture existing or historical variations in entity and producttitles, but to also enable consolidation of future variants of the sameentity, product or service promoted online. The word graph based processdescribed herein can capture existing variations; and, templatesextracted from the word graphs can be used to consolidate unseen futureexamples. Titles of the same entity, product or service have severalwords in common, mostly occurring in the same order or along with thesame neighboring words. Typically, the variations in the titles areeither the attributes or the model versions. Since websites often havestandard templatized representations of entity, product or servicenames, the location of such variations within a title for a givenentity, product or service often follows a pattern.

For example, consider the following original item names: “toddler boysshort sleeve shirt cat jack yellow”, “toddler boys long sleeve shirt catjack black”, “toddler boys short sleeve shirts cat jack navy”, and“toddler boys short sleeve shirt cat jack black”. These names can beconsolidated into a common name, “toddler boys {long, short} sleeve{shirt, shirts} cat jack {black, navy, yellow}”. FIG. 5 shows a visualrepresentation of a normalized word tree formed using the aforesaidoriginal item names. In fact, this is a special kind of tree called asuffix tree or a trie. FIG. 6 shows another type of trie that representsthe consolidated string, “toddler boys {long, short} sleeve {shirt,shirts} cat jack {black, navy, yellow}”. This consolidated string can beconsidered an illustration of a DAWG.

The consolidated string is a compact representation of all of the titlesrepresented by the trie. Such a compact representation constructed usingsuffix trees can be considered a DAWG. Such a data structure can allowfaster searches than other and more conventional tree data structures.The DAWG is sometimes referred to as a deterministic acyclic finitestate automaton (DAFSA). For the purposes of this disclosure, a DAWG canbe or include a DAFSA and represent suffixes of a given string in whicheach edge is labeled with a character or a word. The characters or wordsalong a path from the root to a node are the substring which the noderepresents. Traditionally, this approach is used to represent a compactversion of a trie. A trie uses a node for each occurrence of a term,whereas a DAFSA takes eliminates redundancy occurring in such a trie.DAWG is a compact representation of the corresponding trie since it usesfewer nodes to represent the same set of words. In some embodimentsdescribed herein, the system applies DAWG to complete title phrases orthe heads of titles, and each node of the trie or the DAWG represents aword token in a title or a head of a title.

The technologies described herein can include a process that starts witha normalized trie representation of item names (such as shown in FIG.5). The initial trie can include nodes that capture word tokens andedges that represent the ordering. Also, terms can be coalesced as longas the preceding terms and the succeeding terms are identical across allthe sentences in the trie. In the trie, this translates to the parentand the child nodes linked to a given node. For instance, for allunordered pairs of node children, if their associated children areset-equivalent, the two node children can be coalesced. If the pair ofnode children are childless, they can also be coalesced. By recursivelyapplying the last two mentioned collapsing rules, the system can derivea DAWG as shown in FIG. 6.

Certain embodiments will now be described in greater detail withreference to the FIGS. 1-6. In general, with reference to FIG. 1, asystem 100 in accordance with an embodiment of the present disclosure isshown. FIG. 1 shows components of a general environment in which thesystems and methods discussed herein can be practiced. Not all thecomponents can be required to practice the disclosure, and variations inthe arrangement and type of the components can be made without departingfrom the spirit or scope of the disclosure. As shown, system 100 of FIG.1 includes local area networks (“LANs”)/wide area networks(“WANs”)—network 105 and client devices 102-104 (e.g., such as handheldor mobile devices, Internet of Things devices, etc.). Applications usedby the client devices 102-104 can be served by the servers illustratedin FIG. 1, and such applications can be enhanced by a template generatorthat can implement template generation using DAWGs. The structure of thetemplate generator is further described with reference to templategenerator 244 depicted in FIG. 2.

As shown, system 100 of FIG. 1 also includes application server 106communicatively coupled to database 108 used by the application server,and template generator server 110 that can persistently store thetemplate generator described herein and can provide the templategenerator to other computing devices for being stored in memory of thosedevices or persistently stored in those devices. Also, as shown, system100 of FIG. 1 includes database 112 communicatively coupled to templategenerator server 110 and used by the template generator server, and athird server 114 communicatively coupled to database 116 use by thethird server. The databases described herein can be used by the serversto select, store and organize data used as input for the processesdescribed herein. For example, the feeds described herein can be fromone or more of the databases described herein. The client devicesdescribed herein can also select and use the data stored and organizedin the databases as input for the processes described herein.

It is to be understood that the processes described herein can beexecuted by one or more of the client devices and servers disclosedherein. Specifically, for example, each of the servers 106, 110, and 114can include a device that includes a configuration to perform at leastsome of the operations of process 300 depicted in FIG. 3 and process 400illustrated in FIG. 4. Also, for example, each of the client devices102-104 can include a device that includes a configuration to perform atleast some of the operations of processes 300 and 400. Exampleembodiments of client devices 102-104 and servers 106, 110, and 114 aredescribed in more detail below.

Generally, client devices 102-104 can include virtually any computingdevice capable of receiving and sending a message over a network, suchas network 105—which could include a wireless network—, or the like.Client devices 102-104 can also be mobile devices that are configured tobe portable and held in a hand or two hands. Such devices includemulti-touch and portable devices such as, cellular telephones, smartphones, display pagers, radio frequency (RF) devices, infrared (IR)devices, Personal Digital Assistants (PDAs), handheld computers, laptopcomputers, wearable computers, smart watch, tablet computers, phablets,integrated devices combining one or more of the preceding devices, andthe like. As such, mobile devices typically range widely in terms ofcapabilities and features. For example, a cell phone can have a numerickeypad and a few lines of monochrome LCD display on which only text canbe displayed. In another example, a web-enabled mobile device can have atouch sensitive screen, a stylus, and an HD display in which both textand graphics can be displayed.

A web-enabled client device can include a browser application that isconfigured to receive and to send web pages, web-based messages, and thelike. The browser application can be configured to receive and displaygraphics, text, multimedia, and the like, employing virtually any webbased language, including a wireless application protocol messages(WAP), and the like. In one embodiment, the browser application isenabled to employ Handheld Device Markup Language (HDML), WirelessMarkup Language (WML), WMLScript, JavaScript, Standard GeneralizedMarkup Language (SMGL), HyperText Markup Language (HTML), eXtensibleMarkup Language (XML), and the like, to display and send a message.

Client devices 102-104 and the servers 106, 110, and 114 can eachinclude at least one client application that is configured to receivecontent or data from another computing device. The client applicationcan include a capability to provide and receive textual content,graphical content, audio content, authentication and keying information,and the like. The client application can further provide informationthat identifies itself, including a type, capability, name, and thelike. In one embodiment, client devices 102-104 and the servers 106,110, and 114 can each uniquely identify themselves through any of avariety of mechanisms. Client devices can be identifiable via a phonenumber, Mobile Identification Number (MIN), an electronic serial number(ESN), or another type of device identifier. Servers can be identifiablevia an electronic serial number (ESN) or another type of deviceidentifier.

In general, client devices 102-104 and servers 106, 110, and 114 can becapable of sending or receiving signals, such as via a wired or wirelessnetwork, or can be capable of processing or storing signals, such as inmemory as physical memory states.

Network 105 is configured to couple devices 102-104 and servers 106,110, and 114, or the like, with other computing devices. Network 105 isenabled to employ any form of computer readable media for communicatinginformation from one electronic device to another. Also, network 105 caninclude the Internet in addition to local area networks (LANs), widearea networks (WANs), direct connections, such as through a universalserial bus (USB) port, other forms of computer-readable media, or anycombination thereof. On an interconnected set of LANs, including thosebased on differing architectures and protocols, a router acts as a linkbetween LANs, enabling messages to be sent from one to another, and/orother computing devices.

Within the communications networks utilized or understood to beapplicable to the present disclosure, such networks will employ variousprotocols that are used for communication over the network. Signalpackets communicated via a network, such as a network of participatingdigital communication networks, can be compatible with or compliant withone or more protocols. Signaling formats or protocols employed caninclude, for example, TCP/IP, UDP, QUIC (Quick UDP Internet Connection),DECnet, NetBEUI, IPX, APPLETALK™, or the like. Versions of the InternetProtocol (IP) can include IPv4 or IPv6. The Internet refers to adecentralized global network of networks. The Internet includes localarea networks (LANs), wide area networks (WANs), wireless networks, orlong haul public networks that, for example, allow signal packets to becommunicated between LANs. Signal packets can be communicated betweennodes of a network, such as, for example, to one or more sites employinga local network address. A signal packet can, for example, becommunicated over the Internet from a user site via an access nodecoupled to the Internet. Likewise, a signal packet can be forwarded vianetwork nodes to a target site coupled to the network via a networkaccess node, for example. A signal packet communicated via the Internetcan, for example, be routed via a path of gateways, servers, etc. thatcan route the signal packet in accordance with a target address andavailability of a network path to the target address.

In some embodiments, the network 105 can include content distributionnetwork(s) and/or application distribution network(s). A contentdistribution network (CDN) or an application distribution network (ADN)generally refers to a delivery system that includes a collection ofcomputers or computing devices linked by a network or networks. A CDN orADN can employ software, systems, protocols or techniques to facilitatevarious services, such as storage, caching, communication of content, orstreaming media or applications. A CDN or ADN can also enable an entityto operate or manage another's site infrastructure, in whole or in part.

The servers 106, 110, and 114 can include a device that includes aconfiguration to provide content such as interactive content via anetwork to another device. Such server(s) can, for example, host a site,service or an associated application, such as, an email platform (e.g.,Yahoo!® Mail), a social networking site, a photo sharing site/service(e.g., Tumblr®), a search platform or site, or a personal user site(such as a blog, vlog, online dating site, and the like) and the like.Such server(s) can also host a variety of other sites, including, butnot limited to business sites, educational sites, dictionary sites,encyclopedia sites, wikis, financial sites, government sites, and thelike. Devices that can operate as such server(s) include personalcomputers desktop computers, multiprocessor systems,microprocessor-based or programmable consumer electronics, network PCs,servers, and the like.

The servers 106, 110, and 114 can further provide a variety of servicesthat include, but are not limited to, streaming and/or downloading mediaservices, search services, email services, photo services, web services,social networking services, news services, third-party services, audioservices, video services, instant messaging (IM) services, SMS services,MMS services, FTP services, voice over IP (VOIP) services, or the like.Such services, for example a mail application and/or email-platform, canbe provided via the application server 108, whereby a user is able toutilize such service upon the user being authenticated, verified oridentified by the service. Examples of content can include videos, text,audio, images, or the like, which can be processed in the form ofphysical signals, such as electrical signals, for example, or can bestored in memory, as physical states, for example.

Also, servers 106, 110, and 114 can include an ad server such as aserver that stores online advertisements for presentation to users. “Adserving” provided by an ad server refers to methods used to place onlineadvertisements on websites, in applications, or other places where usersare more likely to see them, such as during an online session or duringcomputing platform use, for example. Various monetization techniques ormodels can be used in connection with sponsored advertising, includingadvertising associated with user. Such sponsored advertising includesmonetization techniques including sponsored search advertising,non-sponsored search advertising, guaranteed and non-guaranteed deliveryadvertising, ad networks/exchanges, ad targeting, ad serving and adanalytics. Such systems can incorporate near instantaneous auctions ofad placement opportunities during web page creation, (in some cases inless than 500 milliseconds) with higher quality ad placementopportunities resulting in higher revenues per ad. That is advertiserswill pay higher advertising rates when they believe their ads are beingplaced in or along with highly relevant content that is being presentedto users. Reductions in the time needed to quantify a high-quality adplacement offers ad platforms competitive advantages. Thus, higherspeeds and more relevant context detection improve these technologicalfields.

Servers 106, 110, and 114 can be capable of sending or receivingsignals, such as via a wired or wireless network, or can be capable ofprocessing or storing signals, such as in memory as physical memorystates. Devices capable of operating as a server can include, asexamples, dedicated rack-mounted servers, desktop computers, laptopcomputers, set top boxes, integrated devices combining various features,such as two or more features of the foregoing devices, or the like.Servers can vary widely in configuration or capabilities, but generally,a server can include one or more central processing units and memory. Aserver can also include one or more mass storage devices, one or morepower supplies, one or more wired or wireless network interfaces, one ormore input/output interfaces, or one or more operating systems, such asWindows Server, Mac OS X, Unix, Linux, FreeBSD, or the like.

In some embodiments, users are able to access services provided byservers 106, 110, and 114. This can include in a non-limiting example,authentication servers, search servers, email servers, social networkingservices servers, SMS servers, IM servers, MMS servers, exchangeservers, photo-sharing services servers, and travel services servers,via the network 105 using their various client devices. In someembodiments, applications, such as a mail or messaging application(e.g., Yahoo! ® Mail, Yahoo! ® Messenger), a photosharing/user-generated content (UGC) application (e.g., Flickr®,Tumblr®, and the like), a streaming video application (e.g., Netflix®,Hulu®, iTunes®, Amazon Prime®, HBO Go®, and the like), blog, photo orsocial networking application (e.g., Facebook®, Twitter® and the like),search application (e.g., Yahoo! ® Search), and the like, can be hostedby servers 106, 110, and 114. Thus, servers 106, 110, and 114 can storevarious types of applications and application related informationincluding application data and user profile information (e.g.,identifying and behavioral information associated with a user). Itshould also be understood that servers 106, 110, and 114 can also storevarious types of data related to content and services provided by anassociated database. Embodiments exist where the network 105 is alsocoupled with/connected to a Trusted Search Server (TSS) which can beutilized to render content in accordance with the embodiments discussedherein. Embodiments exist where the TSS functionality can be embodiedwithin servers 106, 110, and 114.

Moreover, although FIG. 1 illustrates servers 106, 110, and 114 assingle computing devices, respectively, the disclosure is not solimited. For example, one or more functions of servers 106, 110, and 114can be distributed across one or more distinct computing devices.Moreover, in one embodiment, servers 106, 110, and 114 can be integratedinto a single computing device, without departing from the scope of thepresent disclosure.

FIG. 2 is a schematic diagram illustrating a computing device 200showing an example embodiment of a computing device that can be usedwithin the present disclosure. The computing device 200 can include manymore or less components than those shown in FIG. 2. However, thecomponents shown are sufficient to disclose an illustrative embodimentfor implementing some aspects the present disclosure. The computingdevice can represent, for example, any one or more of the servers orclient devices discussed above in relation to FIG. 1.

As shown in the figure, computing device 200 includes a processing unit(CPU) 222 in communication with a mass memory 230 via a bus 224.Computing device 200 also includes a power supply 226, one or morenetwork interfaces 250, and an input/output interface 260 (which caninclude an audio interface, a display, a keypad, an illuminator, aglobal positioning systems (GPS) receiver, sensors, and an input/outputinterface to such devices).

Power supply 226 provides power to computing device 200. A rechargeableor non-rechargeable battery can be used to provide power. The power canalso be provided by an external power source, such as an AC adapter or apowered docking cradle that supplements and/or recharges a battery.Computing device 200 can optionally communicate with a base station (notshown), or directly with another computing device. Network interface 250includes circuitry for coupling computing device 200 to one or morenetworks, and is constructed for use with one or more communicationprotocols and technologies as discussed above. Network interface 250 issometimes known as a transceiver, transceiving device, or networkinterface card (NIC). The input/output interface 260 can be used forcommunicating with external devices. Input/output interface 260 canutilize one or more communication technologies, such as USB, infrared,Bluetooth™, or the like.

Mass memory 230 includes a RAM 232, a ROM 234, and other storage means.Mass memory 230 illustrates another example of computer storage mediafor storage of information such as computer readable instructions, datastructures, program modules or other data. Mass memory 230 stores abasic input/output system (“BIOS”) 240 for controlling low-leveloperation of computing device 200. The mass memory also stores anoperating system 241 in RAM 232 for controlling the operation ofcomputing device 200. It will be appreciated that this component caninclude a general-purpose operating system such as a version of UNIX, orLINUX™, or a specialized client communication operating system such asWindows Client™, or the Symbian® operating system. The operating systemcan include, or interface with a Java virtual machine module thatenables control of hardware components and/or operating systemoperations via Java application programs.

The mass memory also stores a system browser in RAM 232 for controllingoperations of a system browser 243 and applications 242, such astemplate generator 244 which can perform all or many of the operationsdescribed herein in relation to FIGS. 3-6. The applications 242 can alsoinclude second application 246 that can perform all or some of the stepsof process 400 depicted in FIG. 4 as well as possibly some of theoperations of process 300 in FIG. 3. In some embodiments, secondapplication 246 can perform all or some of the steps of processesdepicted in FIGS. 3-6 in conjunction with template generator 244.

The template generator 244 and/or the second application 242 caninclude, be a part of, or be a non-transitory computer-readable storagemedium tangibly encoded with computer-executable instructions, that whenexecuted by processing unit 222 of computing device 200, performs amethod such as steps of process 300 or steps of process 400.

Memory 230 further includes one or more data stores, which can beutilized by computing device 200 to store, among other things, thesystem browser 243, the applications 242 and/or other data. For example,data stores can be employed to store information that describes variouscapabilities of computing device 200. The information can then beprovided to another device based on any of a variety of events,including being sent as part of a header during a communication, sentupon request, or the like. At least a portion of the capabilityinformation can also be stored on a disk drive or other storage medium(not shown) within computing device 200.

Applications 242 can include computer executable instructions which,when executed by computing device 200 or any of the other serversdescribed herein, transmit, receive, and/or otherwise process text,audio, video, images, and enable telecommunication with other serversand/or another user of another client device. Examples of applicationprograms or “apps” in some embodiments include browsers, calendars,contact managers, task managers, transcoders, photo management, databaseprograms, word processing programs, security applications, spreadsheetprograms, games, search programs, and so forth.

In some embodiments, the computing device 200 can include a processorand a non-transitory computer-readable storage medium for tangiblystoring thereon program logic for execution by the processor, theprogram logic having executable logic for performing the steps ofprocess 300 or 400. For example, it can have executable logic forreceiving a first plurality of entity and/or product titles from a firstplurality of title feeds. It can have executable logic for removingduplicate copies of titles in the first plurality of titles so that theplurality of first titles is transformed into a plurality of uniquetitles. It can have executable logic for sorting the plurality of uniquetitles into a plurality of title category sets. For each category set ofthe plurality of category sets, the program logic can have executablelogic for transforming the respective unique titles belonging to thecategory set into a trie data structure by separating words in therespective unique titles into nodes of the trie data structure. Eachnode of the trie data structure can include one word.

Also, for each title category set of the plurality of title categorysets, the program logic can have executable logic for normalizing thetrie data structure by at least removing duplicates in the trie datastructure and executable logic for transforming the normalized trie datastructure into a directed acyclic word graph (DAWG) data structure. TheDAWG data structure can include a plurality of fixed nodes and aplurality of variable nodes, and each fixed node of the plurality offixed nodes can include one fixed word. And, each variable node of theplurality of variable nodes can include a plurality of alternative wordsrepresenting multiple alternative words for the variable node. Also, foreach title category set of the plurality of title category sets, theprogram logic can have executable logic for generating one or moreunique templates based on the DAWG data structure.

Additionally, for example, the program logic can have executable logicfor receiving a second plurality of titles from the first plurality oftitle feeds and/or a second plurality of title feeds. Also, for eachgenerated unique template of the generated unique templates of theplurality of title category sets, the program logic can have executablelogic for searching the second plurality of titles by using thegenerated unique template as a regular expression to match one or moretitles with the generated unique template. The program logic can haveexecutable logic for performing an action according to the matched oneor more titles. In some embodiments, the action can include associating,in a database, the matched one or more titles with the generated uniquetemplate (such as described with respect to step 408 of process 400.

Having described components of the architecture example employed withinthe disclosed systems and methods, the components' operations withrespect to the disclosed systems and methods will now be described belowwith reference to FIGS. 3-6.

In FIG. 3, process 300 details steps performed by one or more computingdevices (such as one or more of the computing devices described herein),in accordance with some embodiments of the present disclosure.Specifically, the steps of process 300 can be performed by a templategenerator running on one or more computing devices (such as the templategenerator 244). The steps are for template generation using directedacyclic word graphs (DAWGs). Process 300 begins with step 302, whichincludes a template generator, such as template generator 244, or one ormore other parts of one or more computing devices (such as computingdevice 200 depicted in FIG. 2), receiving a first plurality of entityand/or product titles from a first plurality of title feeds. The feedscan be received from one or more servers (e.g., servers 106, 110, and114) or one or more client devices (e.g., client devices 102-104) over anetwork (such as network 105).

In step 304, the template generator (or one or more other parts of oneor more computing devices) removes duplicate copies of titles in thefirst plurality of titles so that the plurality of first titles istransformed into a plurality of unique titles.

In step 306, the template generator (or one or more other parts of oneor more computing devices) sorts the plurality of unique titles into aplurality of title category sets.

In step 308, the template generator (or one or more other parts of oneor more computing devices), for each title category set of the pluralityof title category sets, transforms the respective unique titlesbelonging to the title category set into a trie data structure byseparating words in the respective unique titles into nodes of the triedata structure. Each node of the trie data structure can include oneword. The transforming of the respective unique titles belonging to thetitle category set into the trie data structure can include generatingsub-trie data structures of the trie data structure in parallel and/orsimultaneously.

In step 310, the template generator (or one or more other parts of oneor more computing devices), for each title category set of the pluralityof title category sets, normalizes the trie data structure by at leastremoving duplicates in the trie data structure.

In step 312, the template generator (or one or more other parts of oneor more computing devices), for each title category set of the pluralityof title category sets, transforms the normalized trie data structureinto a DAWG data structure. The DAWG data structure can include aplurality of fixed nodes and a plurality of variable nodes. Each fixednode of the plurality of fixed nodes can include one fixed word. And,each variable node of the plurality of variable nodes can include aplurality of alternative words representing multiple alternative wordsfor the variable node.

In step 314, the template generator (or one or more other parts of oneor more computing devices), for each title category set of the pluralityof title category sets, generates one or more unique templates based onthe DAWG data structure. As shown, if there is a category set that hasnot been processed, it will be processed according to steps 308-314.Also, as shown, if all the category sets have been processed accordingto steps 308-314, then process 300 ends until another plurality oftitles from feeds is received for generating templates.

In general, the generating of the one or more unique templates based onthe DAWG data structure can include including a certain number ofwildcard parameters for a corresponding certain number of additionalwords into each unique template of the one or more unique templates,such that the unique template can be used as a certain n-gram.Specifically, for example, the generating of the one or more uniquetemplates based on the DAWG data structure in step 314 can includeincluding only two fixed words of two fixed nodes of the plurality offixed nodes into each unique template of the one or more uniquetemplates along with at least one wildcard parameter for one additionalword. Also, the generating of the one or more unique templates based onthe DAWG data structure can include including only one wildcardparameter for one additional word into each unique template of the oneor more unique templates, such that the unique template can be used as atrigram. The generating the one or more unique templates based on theDAWG data structure can also include including only two wildcardparameters for two additional words into each unique template of the oneor more unique templates, such that the unique template can be used as a4-gram.

In some embodiments, for a given entity and/or product set, thefollowing algorithm can generate a DAWG data structure alone or alongwith aspects of process 300. First, embed item names in a word-leveltrie structure, and preserving common prefix associations as shown inFIG. 5. Second, starting from the root node, follow a post-orderdepth-first traversal of the graph, performing a minimization check ateach node. The check can include, for all unordered pairs of nodechildren (n choose 2), if their associated children are set-equivalent,the two node children can be coalesced. And, if this pair of nodechildren are childless, they can also be coalesced. The check can alsoinclude, combining siblings that are not identical, wherein the originaltrie did not have identically labeled siblings. The minimization checkcan also include, in the event of a coalesce operation and the subtreeoriginating in root has been rearranged, iterate through the subtree todiscover additional matches.

The resulting DAWG can allow representation of each original item namewith a canon form composed of traversing the final graph through a pathwhose node labels match the item name tokens. For instance, theresulting collapse of the sample normalized trie in FIG. 5 can result inthe DAWG as shown in FIG. 6. When the approach uses the entire datasetof titles as a trie, the approach can be parallelized since eachsub-trie can be processed independently. Also, such a technique whenonly the heads of titles are parsed and transformed into DAWGs.

In FIG. 4, process 400 details steps performed by one or more computingdevices (such as one or more of the computing devices described herein),in accordance with some embodiments of the present disclosure.Specifically, the steps of process 400 can be performed by a templategenerator or a second application running on one or more computingdevices (such as the template generator 244 or second application 246).The steps are for uses of the templates generated in process 300.Process 400 begins with step 402, which includes a template generator,such as template generator 244, or a second application, such as secondapplication 246, or one or more other parts of one or more computingdevices (such as computing device 200 depicted in FIG. 2), receiving asecond plurality of entity and/or product titles from the firstplurality of title feeds from process 300 and/or a second plurality oftitle feeds. The feeds can be received from one or more servers (e.g.,servers 106, 110, and 114) or one or more client devices (e.g., clientdevices 102-104) over a network (such as network 105).

In step 404, the template generator or the second application (or one ormore other parts of one or more computing devices), for each generatedunique template of the generated unique templates of the plurality oftitle category sets derived from process 300, searches the secondplurality of titles by using the generated unique template as a regularexpression to match one or more titles with the generated uniquetemplate.

In step 406, the template generator or the second application (or one ormore other parts of one or more computing devices) performs an actionaccording to the matched one or more titles. In step 408, the actionincludes associating, in a database, the matched one or more titles withthe generated unique template. As shown, if there is a templatedgenerated from process 300 that has not been processed, it will beprocessed according to steps 404-408. Also, as shown, if all thegenerated templates have been processed according to steps 404-408, thenprocess 400 ends until another plurality of titles from feeds isreceived for using the templates generated in process 300.

In some examples, a given title of the second plurality of titles canmatch multiple generated unique templates of the generated uniquetemplates of the plurality of title category sets, and in such instancesthe process 400 can further include associating, in a database, thegiven title with only one of the multiple generated unique templatesaccording to a criterion. The associating of the given title with theonly one of the multiple generated unique templates according to thecriterion can include selecting the one of the multiple generated uniquetemplates that has been associated, in the database, with more titlesthan the other templates of the multiple generated unique templates.

Applying DAWG allows representation of each original item name with acanonical form generated by traversing the final graph through a pathwhose node labels match the item name tokens. For instance, the twoitems [t₁_t₂₁_t₃_t₄_t₅₁] and [t₁_t₂₂_t₃_t₄_t₅₂] can be represented by acommon canonical form t₁_{t₂₁, t₂₂}_t₃_t₄_{t₅₁, t₅₂}. Thisrepresentation is composed of single-word token, followed by anaggregate token of two words, followed by two single-word tokens, andending in an aggregate token of two words. While the resulting stringcan represent four different items, only two such items actually appearin the entity and/or product listing. This demonstrates the ability ofthis approach to embed possibly unseen or rare future entities and/orproducts. Thus, generating such templates out of the canonical form canprovide templates for unseen and possibly future entities and/orproducts.

Once equipped with DAWG canonicalized templates, the system can matchany item to one or more templates. Regular expression matching of thetoken words can be used. And, in some examples, regular expressions cantreat each as a wildcard. In the case of trigram templates, the systemtests for a match along a moving window of trigram sequences in acandidate item name. This is less restrictive than end-to-end matching,but suffers from lower precision. In either case, the system bypassesitems with two or less tokens.

In some embodiments, a series of tokens t_(k) can undergo the followingtransformation: ({t_(a1), t_(a2)}-t_(b)-t_(c)-{t_(d1), t_(d2),t_(d3)})→[*-t_(b)-t_(c)-*]. A more aggressive alternative is to generatetrigram templates with two single-word tokens and one multi-word token,again treating multi-word tokens as wildcards: ({t_(a1),t_(a2)}-t_(b)-t_(c)-{t_(d1), t_(d2), t_(d3)})→[*-t_(b)-t_(c)][t_(b)-t_(c)-*]. Each of these templates represent a cluster of six items.

In the event that multiple templates match a candidate item, the systemcan break the tie by taking a template that represents the largestcluster of original items. Also, the system can enforce token-wiseequivalence, treating wildcards as expected. In the event that multipletemplates match an item, the system can use other tie-breaking schemesas alternatives to the largest cluster canon. For example, a randomcanon or largest token set intersection canon can be used.

For the purposes of this disclosure a module is a software, hardware, orfirmware (or combinations thereof) system, process or functionality, orcomponent thereof, that performs or facilitates the processes, features,and/or functions described herein (with or without human interaction oraugmentation). A module can include sub-modules. Software components ofa module can be stored on a computer readable medium for execution by aprocessor. Modules can be integral to one or more servers, or be loadedand executed by one or more servers. One or more modules can be groupedinto an engine or an application.

For the purposes of this disclosure the term “user”, “subscriber”“consumer” or “customer” should be understood to refer to a user of anapplication or applications as described herein and/or a consumer ofdata supplied by a data provider. By way of example, and not limitation,the term “user” or “subscriber” can refer to a person who receives dataprovided by the data or service provider over the Internet in a browsersession, or can refer to an automated software application whichreceives the data and stores or processes the data.

Those skilled in the art will recognize that the methods and systems ofthe present disclosure can be implemented in many manners and as suchare not to be limited by the foregoing exemplary embodiments andexamples. In other words, functional elements being performed by singleor multiple components, in various combinations of hardware and softwareor firmware, and individual functions, can be distributed among softwareapplications at either the client level or server level or both. In thisregard, any number of the features of the different embodimentsdescribed herein can be combined into single or multiple embodiments,and alternate embodiments having fewer than, or more than, all of thefeatures described herein are possible.

Functionality can also be, in whole or in part, distributed amongmultiple components, in manners now known or to become known. Thus,myriad software/hardware/firmware combinations are possible in achievingthe functions, features, interfaces and preferences described herein.Moreover, the scope of the present disclosure covers conventionallyknown manners for carrying out the described features and functions andinterfaces, as well as those variations and modifications that can bemade to the hardware or software or firmware components described hereinas would be understood by those skilled in the art now and hereafter.

Furthermore, the embodiments of methods presented and described asflowcharts in this disclosure are provided by way of example in order toprovide a more complete understanding of the technology. The disclosedmethods are not limited to the operations and logical flow presentedherein. Alternative embodiments are contemplated in which the order ofthe various operations is altered and in which sub-operations describedas being part of a larger operation are performed independently.

While various embodiments have been described for purposes of thisdisclosure, such embodiments should not be deemed to limit the teachingof this disclosure to those embodiments. Various changes andmodifications can be made to the elements and operations described aboveto obtain a result that remains within the scope of the systems andprocesses described in this disclosure.

What is claimed is:
 1. A method comprising: receiving a first pluralityof titles from a first plurality of title feeds; removing duplicatecopies of titles in the first plurality of titles so that the pluralityof first titles is transformed into a plurality of unique titles;sorting the plurality of unique titles into a plurality of categorysets; and for each category set of the plurality of category sets:transforming the respective unique titles belonging to the category setinto a trie data structure by separating words in the respective uniquetitles into nodes of the trie data structure, each node of the trie datastructure comprising one word; normalizing the trie data structure by atleast removing duplicates in the trie data structure; transforming thenormalized trie data structure into a directed acyclic word graph (DAWG)data structure, the DAWG data structure comprising a plurality of fixednodes and a plurality of variable nodes, each fixed node of theplurality of fixed nodes comprising one fixed word, and each variablenode of the plurality of variable nodes comprising a plurality ofalternative words representing multiple alternative words for thevariable node; and generating one or more unique templates based on theDAWG data structure.
 2. The method of claim 1, further comprising:receiving a second plurality of titles from feeds selected from thegroup of title feeds consisting of the first plurality of title feedsand a second plurality of title feeds; and for each generated uniquetemplate of the generated one or more unique templates of the pluralityof category sets, searching the second plurality of titles by using thegenerated unique template as a regular expression to match one or moretitles with the generated unique template.
 3. The method of claim 2,further comprising: performing an action according to the matched one ormore titles.
 4. The method of claim 3, wherein the action comprisesassociating, in a database, the matched one or more titles with thegenerated unique template.
 5. The method of claim 2, further comprising:associating, in a database, a given title of the second plurality oftitles that matches multiple generated unique templates of the generatedone or more unique templates of the plurality of category sets with onlyone of the multiple generated unique templates according to a criterion.6. The method of claim 5, wherein the criterion comprises selecting oneof the multiple generated unique templates that has been associated, inthe database, with more titles than the other templates of the multiplegenerated unique templates.
 7. The method of claim 1, wherein thetransforming the respective unique titles belonging to the category setinto the trie data structure comprises generating sub-trie datastructures of the trie data structure in parallel.
 8. The method ofclaim 1, wherein the generating the one or more unique templates basedon the DAWG data structure comprises including only two fixed words oftwo fixed nodes of the plurality of fixed nodes into each uniquetemplate of the one or more unique templates along with at least onewildcard parameter for one additional word.
 9. The method of claim 8,wherein the generating the one or more unique templates based on theDAWG data structure comprises including only one wildcard parameter forone additional word into each unique template of the one or more uniquetemplates, such that the unique template can be used as a trigram. 10.The method of claim 8, wherein the generating the one or more uniquetemplates based on the DAWG data structure comprises including only twowildcard parameters for two additional words into each unique templateof the one or more unique templates, such that the unique template canbe used as a 4-gram.
 11. A non-transitory computer-readable storagemedium tangibly encoded with computer-executable instructions, that whenexecuted by a processor of computing device, performs a method, themethod comprising: receiving a first plurality of titles from a firstplurality of title feeds; sorting the first plurality of titles into aplurality of category sets; and for each category set of the pluralityof category sets: transforming the respective titles belonging to thecategory set into a trie data structure by separating words in therespective titles into nodes of the trie data structure, each node ofthe trie data structure comprising one word; normalizing the trie datastructure by at least removing duplicates in the trie data structure;and transforming the normalized trie data structure into a directedacyclic word graph (DAWG) data structure, the DAWG data structurecomprising a plurality of fixed nodes and a plurality of variable nodes,each fixed node of the plurality of fixed nodes comprising one fixedword, and each variable node of the plurality of variable nodescomprising a plurality of alternative words representing multiplealternative words for the variable node.
 12. The non-transitorycomputer-readable storage medium of claim 11, wherein the transformingthe respective titles belonging to the category set into the trie datastructure comprises generating sub-trie data structures of the trie datastructure in parallel.
 13. The non-transitory computer-readable storagemedium of claim 11, wherein the method further comprises: for eachcategory set of the plurality of category sets, generating one or moreunique templates based on the respective DAWG data structure of thecategory set.
 14. The non-transitory computer-readable storage medium ofclaim 13, further comprising: receiving a second plurality of titlesfrom feeds selected from the group of title feeds consisting of thefirst plurality of title feeds and a second plurality of title feeds;and for each generated unique template of the generated one or moreunique templates of the plurality of category sets, searching the secondplurality of titles by using the generated unique template as a regularexpression to match one or more titles with the generated uniquetemplate.
 15. The non-transitory computer-readable storage medium ofclaim 13, wherein the generating the one or more unique templates basedon the DAWG data structure comprises including only two fixed words oftwo fixed nodes of the plurality of fixed nodes into each uniquetemplate of the one or more unique templates along with at least onewildcard parameter for one additional word.
 16. The non-transitorycomputer-readable storage medium of claim 15, wherein the generating theone or more unique templates based on the DAWG data structure comprisesincluding only one wildcard parameter for one additional word into eachunique template of the one or more unique templates, such that theunique template can be used as a trigram.
 17. A computing device,comprising: a processor; and a non-transitory computer-readable storagemedium for tangibly storing thereon program logic for execution by theprocessor, the program logic comprising: executable logic for receivinga first plurality of titles from a first plurality of title feeds;executable logic for sorting the first plurality of titles into aplurality of category sets; and for each category set of the pluralityof category sets: executable logic for transforming the respectivetitles belonging to the category set into a trie data structure byseparating words in the respective titles into nodes of the trie datastructure, each node of the trie data structure comprising one word;executable logic for transforming the trie data structure into adirected acyclic word graph (DAWG) data structure, the DAWG datastructure comprising a plurality of fixed nodes and a plurality ofvariable nodes, each fixed node of the plurality of fixed nodescomprising one fixed word, and each variable node of the plurality ofvariable nodes comprising a plurality of alternative words representingmultiple alternative words for the variable node; and executable logicfor generating one or more unique templates based on the DAWG datastructure.
 18. The computing device of claim 17, wherein thetransforming the respective titles belonging to the category set intothe trie data structure comprises generating sub-trie data structures ofthe trie data structure in parallel.
 19. The computing device of claim17, wherein the program logic further comprises: executable logic forreceiving a second plurality of titles from feeds selected from thegroup of title feeds consisting of the first plurality of title feedsand a second plurality of title feeds; and for each generated uniquetemplate of the generated one or more unique templates of the pluralityof category sets: executable logic for searching the second plurality oftitles by using the generated unique template as a regular expression tomatch one or more titles with the generated unique template.
 20. Thecomputing device of claim 17, wherein the generating the one or moreunique templates based on the DAWG data structure comprises includingonly two fixed words of two fixed nodes of the plurality of fixed nodesinto each unique template of the one or more unique templates along withonly one wildcard parameter for one additional word, such that theunique template can be used as a trigram.